Class 09: Halloween
Destiny (A16340362)
candy_file <- "candy-data (1).csv"
candy = read.csv(candy_file, row.names=1)
head(candy)## chocolate fruity caramel peanutyalmondy nougat crispedricewafer
## 100 Grand 1 0 1 0 0 1
## 3 Musketeers 1 0 0 0 1 0
## One dime 0 0 0 0 0 0
## One quarter 0 0 0 0 0 0
## Air Heads 0 1 0 0 0 0
## Almond Joy 1 0 0 1 0 0
## hard bar pluribus sugarpercent pricepercent winpercent
## 100 Grand 0 1 0 0.732 0.860 66.97173
## 3 Musketeers 0 1 0 0.604 0.511 67.60294
## One dime 0 0 0 0.011 0.116 32.26109
## One quarter 0 0 0 0.011 0.511 46.11650
## Air Heads 0 0 0 0.906 0.511 52.34146
## Almond Joy 0 1 0 0.465 0.767 50.34755Q1. How many different candy types are in this dataset?
nrow(candy)## [1] 85Q2. How many fruity candy types are in the dataset?
sum(candy$fruity)## [1] 38Q3. What is your favorite candy in the dataset and what is it’s winpercent value?
candy["Starburst", ]$winpercent## [1] 67.03763Q4. What is the winpercent value for “Kit Kat”?
candy["Kit Kat", ]$winpercent## [1] 76.7686Q5. What is the winpercent value for “Tootsie Roll Snack Bars”?
candy["Tootsie Roll Snack Bars", ]$winpercent## [1] 49.6535library(skimr)
skim(candy)| Name | candy |
| Number of rows | 85 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| chocolate | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| fruity | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| caramel | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| peanutyalmondy | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| nougat | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| crispedricewafer | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| hard | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| bar | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| pluribus | 0 | 1 | 0.52 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| sugarpercent | 0 | 1 | 0.48 | 0.28 | 0.01 | 0.22 | 0.47 | 0.73 | 0.99 | ▇▇▇▇▆ |
| pricepercent | 0 | 1 | 0.47 | 0.29 | 0.01 | 0.26 | 0.47 | 0.65 | 0.98 | ▇▇▇▇▆ |
| winpercent | 0 | 1 | 50.32 | 14.71 | 22.45 | 39.14 | 47.83 | 59.86 | 84.18 | ▃▇▆▅▂ |
Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?
-Yes there are variables/ columns that do look like they’re on a different scale. Fpr example for the variable winpercent
Q7. What do you think a zero and one represent for the candy$chocolate column?
-The one represents that the candy does contain chocolate, but the zero means that the candy doesn’t contain chocolate
Q8. Plot a histogram of winpercent values
hist(candy$winpercent)
Q9. Is the distribution of winpercent values symmetrical?
-No, the winpercent values aren’t symmetrical
Q10. Is the center of the distribution above or below 50%?
-The distribution of the center is below 50%
Q11. On average is chocolate candy higher or lower ranked than fruit candy?
-On average, chocolate is ranked higher than the fruit candy.
candy$chocolate## [1] 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 1 1 1
## [39] 1 1 1 0 1 1 0 0 0 1 0 0 0 1 1 1 1 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1
## [77] 1 1 0 1 0 0 0 0 1choc.Inds <- as.logical(candy$chocolate)
choc.win <- candy[choc.Inds, ]$winpercent
mean(choc.win)## [1] 60.92153fruity.Ind <- as.logical(candy$fruity)
fruity.win <- candy[fruity.Ind, "winpercent"]
mean(fruity.win)## [1] 44.11974Q12. Is this difference statistically significant?
-The difference is statistically significant
t.test(choc.win, fruity.win)##
## Welch Two Sample t-test
##
## data: choc.win and fruity.win
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.44563 22.15795
## sample estimates:
## mean of x mean of y
## 60.92153 44.11974Q13. What are the five least liked candy types in this set?
-Nik L Nip, Boston Baked Beans, Chiclets, Super Bubble , Jawbusters
head(candy[order(candy$winpercent),], n=5)## chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip 0 1 0 0 0
## Boston Baked Beans 0 0 0 1 0
## Chiclets 0 1 0 0 0
## Super Bubble 0 1 0 0 0
## Jawbusters 0 1 0 0 0
## crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip 0 0 0 1 0.197 0.976
## Boston Baked Beans 0 0 0 1 0.313 0.511
## Chiclets 0 0 0 1 0.046 0.325
## Super Bubble 0 0 0 0 0.162 0.116
## Jawbusters 0 1 0 1 0.093 0.511
## winpercent
## Nik L Nip 22.44534
## Boston Baked Beans 23.41782
## Chiclets 24.52499
## Super Bubble 27.30386
## Jawbusters 28.12744Q14. What are the top 5 all time favorite candy types out of this set?
head(candy[order(-candy$winpercent),], n=5)## chocolate fruity caramel peanutyalmondy nougat
## Reese's Peanut Butter cup 1 0 0 1 0
## Reese's Miniatures 1 0 0 1 0
## Twix 1 0 1 0 0
## Kit Kat 1 0 0 0 0
## Snickers 1 0 1 1 1
## crispedricewafer hard bar pluribus sugarpercent
## Reese's Peanut Butter cup 0 0 0 0 0.720
## Reese's Miniatures 0 0 0 0 0.034
## Twix 1 0 1 0 0.546
## Kit Kat 1 0 1 0 0.313
## Snickers 0 0 1 0 0.546
## pricepercent winpercent
## Reese's Peanut Butter cup 0.651 84.18029
## Reese's Miniatures 0.279 81.86626
## Twix 0.906 81.64291
## Kit Kat 0.511 76.76860
## Snickers 0.651 76.67378Q15. Make a first barplot of candy ranking based on winpercent values.
library(ggplot2)
ggplot(candy) +
aes(winpercent, rownames(candy)) +
geom_col()
>Q16. This is quite ugly, use the reorder() function to get the bars
sorted by winpercent?
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col()
my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "chocolate"
my_cols[as.logical(candy$bar)] = "brown"
my_cols[as.logical(candy$fruity)] = "pink"ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col(fill=my_cols) 
Q17. What is the worst ranked chocolate candy?
-The worst ranked chocolate candy is Sixlets
Q18. What is the best ranked fruity candy?
-The best ranked fruity candy is Starbursts
- Taking a look at pricepercent
library(ggrepel)
# How about a plot of price vs win
ggplot(candy) +
aes(winpercent, pricepercent, label=rownames(candy)) +
geom_point(col=my_cols) +
geom_text_repel(col=my_cols, size=3.3, max.overlaps = 25)
Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck?
ord <- order(candy$pricepercent, decreasing = FALSE)
head( candy[ord,c(11,12)], n=5 )## pricepercent winpercent
## Tootsie Roll Midgies 0.011 45.73675
## Pixie Sticks 0.023 37.72234
## Dum Dums 0.034 39.46056
## Fruit Chews 0.034 43.08892
## Strawberry bon bons 0.058 34.57899Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?
ord <- order(candy$pricepercent, decreasing = TRUE)
head( candy[ord,c(11,12)], n=5 )## pricepercent winpercent
## Nik L Nip 0.976 22.44534
## Nestle Smarties 0.976 37.88719
## Ring pop 0.965 35.29076
## Hershey's Krackel 0.918 62.28448
## Hershey's Milk Chocolate 0.918 56.490505 Exploring the correlation structure
library(corrplot)## corrplot 0.92 loadedcij <- cor(candy)
corrplot(cij)
>Q22. Examining this plot what two variables are anti-correlated
(i.e. have minus values)?
-Fruity and chocolate are the most anti correlated >Q23. Similarly, what two variables are most positively correlated?
-Chocolate and bar are the most positively correlated
- Principal Component Analysis
pca <-prcomp(candy, scale=TRUE)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion 0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion 0.89998 0.93832 0.97071 0.98683 1.00000plot(pca$x[,1:2])
# Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])p <- ggplot(my_data) +
aes(x=PC1, y=PC2,
size=winpercent/100,
text=rownames(my_data),
label=rownames(my_data)) +
geom_point(col=my_cols)
p
library(ggrepel)
p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7) +
theme(legend.position = "none") +
labs(title="Halloween Candy PCA Space",
subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
caption="Data from 538")## Warning: ggrepel: 39 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(p)par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")
Q24. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you?
-Orignal variables picked up strognly be PC1 in the postive direction is fruity candies and pluribus, and this does make sense since fruit candies are often packaged with multiple other candy pieces, it’s just not typically one by itself.